Executive Summary

This document details the proof-of-concept development and validation of a real-time AI voice agent built on LiveKit’s agent framework. The system combines AssemblyAI for speech-to-text transcription, Groq’s ultra-fast LLM inference for natural language responses, and Silero for voice activity detection — delivering a fully functional, low-latency voice conversation pipeline using only two external API keys.

The POC successfully demonstrated end-to-end voice transcription with the STT pipeline confirmed operational. The system connects participants to a LiveKit room, detects speech via VAD, transcribes audio in real time via AssemblyAI, and routes transcribed text through the Groq LLM for intelligent responses.

The Business Problem

Building production-grade voice AI systems traditionally requires significant infrastructure investment and multiple costly API subscriptions. Key operational challenges include:

  • High API cost burden: Most voice agent frameworks require separate paid services for STT, LLM, and TTS, creating compounding subscription costs
  • Integration complexity: Orchestrating real-time audio pipelines across multiple providers introduces latency, failure points, and debugging difficulty
  • Slow LLM inference: General-purpose LLM providers introduce unacceptable latency for real-time voice conversation where sub-second response is expected
  • Deployment friction: Complex environment setup, dependency conflicts, and platform compatibility issues delay time-to-demo for POC validation

Solution Overview

We developed a streamlined voice agent pipeline that minimises API dependencies while maximising performance. The system leverages Groq’s hardware-accelerated inference (LPU) for near-instant LLM responses and AssemblyAI’s real-time streaming transcription for accurate STT — orchestrated through LiveKit’s production-grade agent framework.

Core Components

ComponentProviderRole
Speech-to-Text (STT)AssemblyAIReal-time audio transcription with streaming support
Large Language ModelGroq (Mixtral 8x7B)Ultra-low latency LLM inference via LPU hardware
Voice Activity DetectionSilero VADLocal, offline speech detection — no API key required
Agent OrchestrationLiveKit AgentsRoom management, audio routing, and session lifecycle
Text-to-Speech (TTS)Groq / OpenAI PluginVoice synthesis via OpenAI-compatible TTS endpoint

Technical Architecture

Operational Pipeline

The system follows a sequential, event-driven pipeline from audio capture through spoken response:

  • Audio Capture: LiveKit room captures participant microphone stream and routes raw PCM audio to the agent process
  • Voice Activity Detection: Silero VAD (loaded locally at prewarm) segments continuous audio into discrete speech utterances, filtering silence
  • Speech Transcription: AssemblyAI STT receives segmented audio and returns transcribed text in real time via streaming API
  • LLM Inference: Transcribed text is passed to the Groq-hosted Mixtral 8x7B model; response generated at hardware-accelerated speed via LPU
  • Response Delivery: Agent response text is synthesised to audio via TTS and played back to the participant in the LiveKit room

Key Design Decisions

  • Minimal API surface: Only 2 external API keys required (Groq + AssemblyAI), reducing cost and credential management overhead
  • Local VAD: Silero VAD runs entirely offline via the livekit-plugins-silero package, eliminating a third API dependency
  • OpenAI-compatible LLM interface: Custom GroqLLM class wraps Groq SDK using the LLM base class pattern, enabling drop-in compatibility with LiveKit’s AgentSession
  • Prewarm function: VAD model loaded at worker initialisation, not per-session, reducing per-call cold start latency

Business Value & Impact

Cost Efficiency

  • Only 2 API keys required, compared to 3–4 for conventional voice agent stacks
  • Silero VAD runs fully offline, eliminating a recurring API cost entirely
  • Groq’s free tier provides sufficient throughput for POC and early production workloads

Performance

  • Groq LPU inference delivers sub-200ms LLM response latency — essential for natural voice conversation
  • AssemblyAI streaming transcription returns partial results in real time, reducing perceived latency
  • VAD prewarming at worker start eliminates per-session model loading overhead

Developer Experience

  • Clean, minimal codebase: single Python file with < 160 lines implements the complete agent pipeline
  • LiveKit’s dev mode with hot-reload accelerates iteration during POC development
  • Standard OpenAI-compatible interface on Groq simplifies future model swaps

Conclusion

This proof-of-concept successfully validated the core architecture of a real-time AI voice agent built on LiveKit, Groq, and AssemblyAI. The transcription pipeline is fully operational, with speech detection, audio routing, and LLM inference all confirmed working in a live room environment.

The outstanding item — TTS voice output — is a known integration issue (silero.TTS() unavailability in the installed plugin version) with a clear resolution path via Groq’s OpenAI-compatible TTS endpoint. All other pipeline components are production-ready.

Based on POC validation, the system is well-positioned for full end-to-end voice conversation once TTS output is wired. The architecture is lean, cost-efficient, and extensible for production deployment.

Demo Video

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